The lithiation process and Li diffusion in amorphous SiO2 and Si from first-principles

Silicon is considered the next-generation, high-capacity anode for Li-ion energy storage applications, however, despite significant effort, there are still uncertainties regarding the bulk Si and surface SiO2 structural and chemical evolution as it undergoes lithiation and amorphization. In this paper, we present first-principles calculations of the evolution of the amorphous Si anode, including its oxide surface layer, as a function of Li concentration. We benchmark our methodology by comparing the results for the Si bulk to existing experimental evidence of local structure evolution, ionic diffusivity as well as electrochemical activity. Recognizing the important role of the surface Si oxide (either native or artificially grown), we undertake the same calculations for amorphous SiO2, analyzing its potential impact on the activity of Si anode materials. Derived voltage curves for the amorphous phases compare well to experimental results, highlighting that SiO2 lithiates at approximately 0.7 V higher than Si in the low Li concentration regime, which provides an important electrochemical fingerprint. The combined evidence suggests that i) the inherent diffusivity of amorphous Si is high (in the order 10−9cm2s−1 - 10−7cm2s−1), ii) SiO2 is thermodynamically driven to lithiate, such that Li–O local environments are increasingly favored as compared to Si–O bonding, iii) the ionic diffusivity of Li in LiySiO2 is initially two orders of magnitude lower than that of LiySi at low Li concentrations but increases rapidly with increasing Li content and iv) the final lithiation product of SiO2 is Li2O and highly lithiated silicides. Hence, this work suggests that - excluding explicit interactions with the electrolyte - the SiO2 surface layer presents a kinetic impediment for the lithiation of Si and a sink for Li inventory, resulting in non-reversible capacity loss through strong local Li–O bond formation

The Phase Diagram of all Inorganic Materials

Understanding how the arrangement of atoms and their interactions determine material behavior has been the dominant paradigm in materials science. A complementary approach is studying the organizational structure of networks of materials, defined on the basis of interactions between materials themselves. In this work, we present the "phase diagram of all known inorganic materials", an extremely-dense complex network of nearly $2.1 \times 10^4$ stable inorganic materials (nodes) connected with $41 \times 10^6$ tie-lines (edges) defining their two-phase equilibria, as computed via high-throughput density functional theory. We show that the degree distribution of this network follows a lognormal form, with each material connected to on average 18% of the other materials in the network via tie-lines. Analyzing the structure and topology of this network has potential to uncover new materials knowledge inaccessible from the traditional bottom-up (atoms to materials) approaches. As an example, we derive a data-driven metric for the reactivity of a material as characterized by its connectedness in the network, and quantitatively identify the noblest materials in nature

Güç kaynakları ve otomotiv elektroniği uygulamaları için bor tabanlı kalın kesitli metalik cam / nanokristal manyetik malzemelerin geliştirilmesi

TÜBİTAK MAG Proje01.06.2008This study is pertinent to setting a connection between glass forming ability (GFA) and topology of Fe-B based metallic glasses, identifying atomic effect order of elements increasing GFA and developing soft magnetic bulk metallic glasses (BMG) / bulk nanocrystalline alloys (BNCA) for industrial applications by combining intimate investigations on spatial atomic arrangements conducted via solid computer simulations with experimentations on high GFA bulk metallic glasses. In order to construct a theoretical framework, the nano-scale phase separation encountered in metallic glasses is investigated for amorphous Fe80B20 and Fe83B17 alloys via Monte Carlo and Reverse Monte Carlo simulations. All topological aspects revealed by developed analysis tools are compiled into a new model called Two-Dimensional Projection Model for predicting contributions to short and medium range order (MRO) and corresponding spacing relations. The outcome geometrically involves proportions approximating golden ratio. Soft magnetic Fe-Co-Nb-B-Si BMG and FeCo-Nb-B-Si-Cu BMG/BNCAs are produced with a totally conventional route, thermally characterized and their magnetic properties are measured. Influences of alloying elements that increase GFA and promote nanocristalization, on structural units and crystallization modes are identified by the developed model and radial distributions. While Co atoms substitute for Fe atoms, Nb and Si atoms deform trigonal prismatic units to provide local compactions at the outset of MRO. The GFA can be described by a new parameter quantifying the MRO compaction, cited as Φ. Moreover, after annealing Fe-Co-Nb-B-Si-Cu BMG alloy at 873 K for 300 s., the the precipitation is altered from Fe23B6 meta-sTablo phase to α-Fe nanocrystals, BNCAs are produced and this phenomenon is investigated structurally. It has been shown that developed Fe-B based BMGs and BNCAs show very good soft magnetic properties and optimum alloy composition is determined as (Fe36Co36B19.2Si4.8Nb4)99.25Cu0.75 with 3 mm thickness, 1.58 T saturation induction and 0.148 Oe coercivity

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

The Open Quantum Materials Database (OQMD) is a high-throughput database currently consisting of nearly 300,000 density functional theory (DFT) total energy calculations of compounds from the Inorganic Crystal Structure Database (ICSD) and decorations of commonly occurring crystal structures. To maximise the impact of these data, the entire database is being made available, without restrictions, at www.oqmd.org/download. In this paper, we outline the structure and contents of the database, and then use it to evaluate the accuracy of the calculations therein by comparing DFT predictions with experimental measurements for the stability of all elemental ground-state structures and 1,670 experimental formation energies of compounds. This represents the largest comparison between DFT and experimental formation energies to date. The apparent mean absolute error between experimental measurements and our calculations is 0.096 eV/atom. In order to estimate how much error to attribute to the DFT calculations, we also examine deviation between different experimental measurements themselves where multiple sources are available, and find a surprisingly large mean absolute error of 0.082 eV/atom. Hence, we suggest that a significant fraction of the error between DFT and experimental formation energies may be attributed to experimental uncertainties. Finally, we evaluate the stability of compounds in the OQMD (including compounds obtained from the ICSD as well as hypothetical structures), which allows us to predict the existence of ~3,200 new compounds that have not been experimentally characterised and uncover trends in material discovery, based on historical data available within the ICSD

Benchmarking the Acceleration of Materials Discovery by Sequential Learning

Sequential learning (SL) strategies, i.e. iteratively updating a machine learning model to guide experiments, have been proposed to significantly accelerate materials discovery and research. Applications on computational datasets and a handful of optimization experiments have demonstrated the promise of SL, motivating a quantitative evaluation of its ability to accelerate materials discovery, specifically in the case of physical experiments. The benchmarking effort in the present work quantifies the performance of SL algorithms with respect to a breadth of research goals: discovery of any “good” material, discovery of all “good” materials, and discovery of a model that accurately predicts the performance of new materials. To benchmark the effectiveness of different machine learning models against these goals, we use datasets in which the performance of all materials in the search space is known from high-throughput synthesis and electrochemistry experiments. Each dataset contains all pseudo-quaternary metal oxide combinations from a set of six elements (chemical space), the performance metric chosen is the electrocatalytic activity (overpotential) for the oxygen evolution reaction (OER). A diverse set of SL schemes is tested on four chemical spaces, each containing 2121 catalysts. The presented work suggests that research can be accelerated by up to a factor of 20 compared to random acquisition in specific scenarios. The results also show that certain choices of SL models are ill-suited for a given research goal resulting in substantial deceleration compared to random acquisition methods. The results provide quantitative guidance on how to tune an SL strategy for a given research goal and demonstrate the need for a new generation of materials-aware SL algorithms to further accelerate materials discovery

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces

The Materials Research Platform: Defining the Requirements from User Stories

A recent meeting focused on accelerated materials design and discovery examined user requirements for a general, collaborative, integrative, and on-demand materials research platform